I develop real-time computer vision systems optimized for production deployment on edge devices like NVIDIA Jetson Nano and Raspberry Pi. From object detection and tracing with YOLOv8 to custom image processing pipelines using OpenCV and TensorRT, I deliver systems that perform reliably in the real world, not just in a lab notebook.
My edge AI solutions are built for performance on constrained hardware. I have achieved 10-15 FPS optimization on resource-limited devices through model quantization, TensorRT acceleration, and efficient pipeline architecture. My production system GoViz demonstrates this capability with real-time collision detection running on embedded hardware with optimized inference pipelines.
Every computer vision project I deliver includes proper model training and validation, inference optimization, event-driven processing with MQTT communication, and integration with your existing infrastructure. I handle the full pipeline from data collection and annotation through model deployment and real-time monitoring, ensuring your vision system works reliably in production conditions.